Multi-Channel Dual-Mode Oil Multi-Pollutant Detection Sensor

Author:

Wang Chenyong1,Zhang Hongpeng1ORCID,Bai Chenzhao1ORCID,Li Wei1,Wang Shengzhao1,Zhang Shuyao2

Affiliation:

1. Marine Engineering College, Dalian Maritime University, Dalian 116026, China

2. Navigation College, Dalian Maritime University, Dalian 116026, China

Abstract

In order to realize the lubricant fluid condition monitoring of ships and offshore engineering equipment, a multi-channel, dual-mode oil multi-pollution detection sensor is proposed and fabricated. The sensor has three detection channels connected via tee tubes, as well as two different detection modes, inductive and capacitive, respectively. In comparison to the traditional sensor, this sensor not only has the ability to distinguish and identify a diverse range of pollutants, but it also experiences an 11-fold increase in its volume of flow, resulting in a significant enhancement in detection efficiency. The mechanism of the inductive and capacitive modes for the differentiated detection of multiple pollutants is elucidated through theoretical analysis. The performance of the sensor is investigated using the constructed experiment platform. The experimental results show that the sensor can realize the simultaneous detection of metallic and non-metallic contaminants in lubricating oil fluids. It can detect the smallest iron particle size of 54 μm, the smallest copper particle size of 90 μm, the smallest water droplet size of 116 μm, and the smallest air bubble size of 130 μm. A novel approach for achieving ship and marine engineering equipment health monitoring and fault diagnosis is presented in this study.

Funder

Natural Science Foundation of China

Liaoning Revitalization Talents Program

Fundamental Research Funds for the Central Universities

Innovative Projects for the Application of Advance Research on Equipment

Science and Technology Innovation Fund of Dalian

Publisher

MDPI AG

Subject

Ocean Engineering,Water Science and Technology,Civil and Structural Engineering

Reference32 articles.

1. Application and prospect of intelligent fault diagnosis technology for marine power plant;Jiang;Chin. J. Ship Res.,2020

2. Multisensory information integration for online wear condition monitoring of diesel engines;Cao;Tribol. Int.,2015

3. Online oil debris monitoring of rotating machinery: A detailed review of more than three decades;Sun;Mech. Syst. Signal Process.,2021

4. Advancement and current status of wear debris analysis for machine condition monitoring: A review;Kumar;Ind. Lubr. Tribol.,2013

5. Influence of solid particles as a contaminants on degradation processes in hydraulic components or systems;Karanovi;Int. J. Ind. Eng. Manag.,2015

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